Multiple phase flow identification using computational simulation and convolutional neural network
The Identification of gas-solid flow characterization in dense-phase pneumatic conveying particles is very important to a vast area of industrial fields such as chemical and pharmaceutical industries since a slight change in flow characteristics results in a completely different product. The motion...
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| التنسيق: | أطروحة |
| اللغة: | English |
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2020
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| الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/93119/1/MohamedTawfikIbrahimMSKE2020.pdf |
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my-utm-ep.931192021-11-19T03:31:23Z Multiple phase flow identification using computational simulation and convolutional neural network 2020 Helmy, Mohamed Tawfik Ibrahim TK Electrical engineering. Electronics Nuclear engineering The Identification of gas-solid flow characterization in dense-phase pneumatic conveying particles is very important to a vast area of industrial fields such as chemical and pharmaceutical industries since a slight change in flow characteristics results in a completely different product. The motion of the gas-solid two-phase flow in densephase usually has a nonlinear and unsteady nature that needs to be examined and analysed to identify the particle flow behaviour in the pneumatic conveying pipelines. In this research a method to identify the type of flow pattern is proposed using a computational method where a gravity flow rig is modelled on Solidworks and multiple flow patterns are simulated with different mass flow rates ranging between 200 to 600 g/s. For changing the flow patterns inside the pipe, an Iris Mechanism is designed according to the specifications of the flow required to achieve the flow pattern control. A sectioning method is implemented to capture flow images at the plane of interest for different flow patterns. Afterwards images are fed to a Convolutional Neural Network which is trained and tested to identify the flowpatterns according to several flowfeatures which resulted in 100% accuracy. A GUI using PyQt is designed to better visualize the whole system and view the predicted flow pattern. 2020 Thesis http://eprints.utm.my/id/eprint/93119/ http://eprints.utm.my/id/eprint/93119/1/MohamedTawfikIbrahimMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135980 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering |
| institution |
Universiti Teknologi Malaysia |
| collection |
UTM Institutional Repository |
| language |
English |
| topic |
TK Electrical engineering Electronics Nuclear engineering |
| spellingShingle |
TK Electrical engineering Electronics Nuclear engineering Helmy, Mohamed Tawfik Ibrahim Multiple phase flow identification using computational simulation and convolutional neural network |
| description |
The Identification of gas-solid flow characterization in dense-phase pneumatic conveying particles is very important to a vast area of industrial fields such as chemical and pharmaceutical industries since a slight change in flow characteristics results in a completely different product. The motion of the gas-solid two-phase flow in densephase usually has a nonlinear and unsteady nature that needs to be examined and analysed to identify the particle flow behaviour in the pneumatic conveying pipelines. In this research a method to identify the type of flow pattern is proposed using a computational method where a gravity flow rig is modelled on Solidworks and multiple flow patterns are simulated with different mass flow rates ranging between 200 to 600 g/s. For changing the flow patterns inside the pipe, an Iris Mechanism is designed according to the specifications of the flow required to achieve the flow pattern control. A sectioning method is implemented to capture flow images at the plane of interest for different flow patterns. Afterwards images are fed to a Convolutional Neural Network which is trained and tested to identify the flowpatterns according to several flowfeatures which resulted in 100% accuracy. A GUI using PyQt is designed to better visualize the whole system and view the predicted flow pattern. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Helmy, Mohamed Tawfik Ibrahim |
| author_facet |
Helmy, Mohamed Tawfik Ibrahim |
| author_sort |
Helmy, Mohamed Tawfik Ibrahim |
| title |
Multiple phase flow identification using computational simulation and convolutional neural network |
| title_short |
Multiple phase flow identification using computational simulation and convolutional neural network |
| title_full |
Multiple phase flow identification using computational simulation and convolutional neural network |
| title_fullStr |
Multiple phase flow identification using computational simulation and convolutional neural network |
| title_full_unstemmed |
Multiple phase flow identification using computational simulation and convolutional neural network |
| title_sort |
multiple phase flow identification using computational simulation and convolutional neural network |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering |
| granting_department |
Faculty of Engineering - School of Electrical Engineering |
| publishDate |
2020 |
| url |
http://eprints.utm.my/id/eprint/93119/1/MohamedTawfikIbrahimMSKE2020.pdf |
| _version_ |
1747818635083120640 |